Protecting Time Series Data with Minimal Forecast Loss. (arXiv:2106.16085v2 [cs.CR] UPDATED)

Forecasting could be negatively impacted due to anonymization requirements in
data protection legislation. To measure the potential severity of this problem,
we derive theoretical bounds for the loss to forecasts from additive
exponential smoothing models using protected data. Following the guidelines of
anonymization from the General Data Protection Regulation (GDPR) and California
Consumer Privacy Act (CCPA), we develop the $k$-nearest Time Series ($k$-nTS)
Swapping and $k$-means Time Series ($k$-mTS) Shuffling methods to create
protected time series data that minimizes the …

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